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Innovative Delirium Detection Methods in ICU Patients

Explore new approaches using EEG, eye movements, and temperature variability to detect delirium in ICU patients and improve outcomes. Study physiological parameters and EEG characteristics to enhance delirium screening accuracy.

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Innovative Delirium Detection Methods in ICU Patients

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  1. Delirium detection in Intensive Care patients Willemijn van der Kooi Department of Intensive Care Medicine University Medical Center Utrecht, The Netherlands

  2. Disclosures • Orion Pharma: contributed to printing costs of my thesis • NPK design: contributed to printing costs of my thesis

  3. Introduction Delirium prevalence: • 50%-80% for ICU patients • 10-15% for cardiac surgery patients ICU delirium is associated with: • Long term cognitive impairment • Increased hospital and ICU length of stay • Increased mortality * Actor

  4. Introduction Delirium often (71%) missed by ICU physicians1 • questionnaires developed for screening Daily practice • Sensitivity of questionnaire with best performance (Cam-ICU): • 47% in ICU patients2 • 28% in post-operative patients3 • Cognitive screening may not fit well in the culture of the ICU • 1 Van Eijk et al. Crit Care Med 2009;37:1881-5 • 2 Van Eijk et al. Am J RespirCrit Care Med 2011;184:340-4 • 3 Neufeld et al. Br J Anaesth 2013;111:612-8

  5. Introduction New approach: delirium detection using physiological alterations • Ultimate goal: • 2 sensors coupled to a monitor • Monitor shows on a scale the chance of having delirium

  6. Content Three physiological parameters studied: • Temperature variability • Eye movements • Brain activity (EEG) Future perspective

  7. Temperature variability during delirium in ICU patients Van der kooi et al. PLoS One. 2013; 8:e78923

  8. Introduction Delirium: manifestation of encephalopathy • In delirium tremens, Wernicke encephalopathy and schizophrenia: temperature regulation is disturbed • Does delirium affect thermoregulation?

  9. Aim of the study To investigate whether: • ICU delirium is related to absolute body temperature • ICU delirium is related to temperature variability

  10. Methods • Subjects from 3 previous delirium studies • Daily delirium assessments by research- nurse/physician Temperature: measured per minute 24/7

  11. Methods Inclusion: • Patients with delirious + non-delirious days during ICU admission of >24 hrs Exclusion criteria: • Disturbed body temperature regulation (treatment/diagnoses) • Neurological/neurosurgical disease • Days with sepsis, coma or death were excluded from analysis *Allpatients received paracetamol 1000 mg 4 times daily

  12. Methods No Delirium Delirium Coma

  13. Methods Linear Mixed models: • Univariable (unadjusted) • Multivariable (adjusted for confounders RASS and SOFA) Outcome: • body temperature [°C] • temperature variability (absolute second derivative) [°C/min2]

  14. Results

  15. Results

  16. Results Body Temperature:

  17. Results Temperature Variability:

  18. Discussion Strengths: • Delirium diagnoses prospectively • Within subjects comparisons • Easy method temperature variability Limitations • Possible effect of medication • Natural circadian rhythm bias

  19. Discussion Temperature variability: increased during delirium in ICU patients • encephalopathy that underlies delirium Future studies: • Monitoring temperature variability in total ICU population • Combine with EEG for objective tool to detect delirium

  20. Delirium detection based on monitoring of blinks and eye movements Van der kooi et al. Am J Geriatr Psychiatry. 2014

  21. Introduction Delirium associated with change in motor level activity • Actigraphy not practical • Eye movements less affected by muscle weakness, restraints, pain

  22. Goal Determine whether eye blinks and eye movements differ in patients with delirium compared to patients without delirium.

  23. Methods Population: post-cardiac surgery patients Reference: psychiatrist, geriatrist, neurologist using DSM 4 criteria

  24. Methods Standard 21 electrode EEG recording (30 minutes) with periods of eyes open and closed First artifact free minute selected with eyes closed and open

  25. Methods: Eye movements Eye movements compared between delirium and non-delirium Number (per min) and duration (sec) of: • Blinks • Vertical eye movements • Horizontal eye movements

  26. Results: study population

  27. Results: eye movements Eyes Open

  28. Results: eye movements Eyes Closed

  29. Results: Eye movements haloperidol

  30. Conclusion Especially blinks are affected in delirious patients Strengths: • non-invasive • Only 1 minute of data necessary Limitations: • 22 electrodes needed for eye movement measurement, except for blinks • Difference in Apache and Charlson Comorbidity score Future studies: • Detection of eye movements in general population of ICU patients • Determining whether eye movements can detect delirium at early stage

  31. Delirium detection using EEG: what and how to measure? Van der kooi et al. Chest. 2014

  32. Introduction Delirium characterized by EEG abnormalities • EEG not practical Without Delirium With Delirium

  33. Goal Determine the electrode derivation and EEG characteristic that have the best capability of discriminating delirium from non-delirium

  34. Methods Standard 21 electrode EEG recording (30 minutes) with periods of eyes open and closed First artifact free minute selected with eyes closed

  35. Methods: EEG Eyes closed= 210 different derivations

  36. Methods: EEG For every derivation 6 parameters: 1 Relative delta power (0.5-4 Hz), Relative theta power (4-8 Hz),Relative alpha power (8-13 Hz), Relative beta power (13-20 Hz), Peak frequency, Slow-fast ratio δ 0-4 Hz θ 4-8 Hz α 8-13 Hz Ruwe EEG β 13-20 Hz 1van der Kooi, et al. J Neuropsychiatry Clin Neurosci 2012; 24: 472-477.

  37. Methods: EEG 210 derivations x 6 parameters = 1260 combinations All 1260 combinations • Compared between delirium and non-delirium (Mann-whitney U) • P-values ranked • smallest p-value is optimal combination (Bonferoni correction ) 1van der Kooi, et al. J Neuropsychiatry Clin Neurosci 2012; 24: 472-477.

  38. Results: EEG *p< 4.0*10-5 is significant

  39. Results: EEG Most optimal electrode locations, based on first 4 rankings.

  40. Conclusion EEG easily detects delirium from non-delirium using • 2 electrodes in frontal-parietal derivation and relative delta power Strengths: new approach, non-invasive, only 2 electrodes and 1 minute data necessary Future studies: • Validation study in unselected population of postoperative- and critically ill patients • Determine whether it recognizes delirium at an early stage

  41. Future Directions

  42. Overall Conclusion EEG most promising method for delirium detection. Project started: Development of delirium monitor

  43. Product development Product and algorithm

  44. Validation study Goal: To determine sensitivity, specificity and predictive values of the delirium monitor when compared to reference standard (specialized geriatric nurse) in elderly postoperative patients (n=154).

  45. Usability study • Practical? • Easy to Use? • Opinion of nurses of different medical departments

  46. Extra slides

  47. Results: EEG eyes open *p< 5.6*10-4 is significant Delirium met/zonder haloperidol geen verschil (p=0.37)

  48. Results: Eye movements eyes open

  49. Results: Eye movements eyes closed

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